Challenge: MRC requires machines to understand text and answer questions about the text.
Approach: They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome: The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Challenge: Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks.
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Improving Machine Reading Comprehension with General Reading Strategies (N19-1)

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Challenge: Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge.
Approach: They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task.
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MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)

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Challenge: MRQA datasets have been used to benchmark progress in general-purpose language understanding.
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Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

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Challenge: Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
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A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (D18-1)

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Challenge: Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources.
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Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data (2021.findings-emnlp)

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Challenge: despite considerable progress, most machine reading comprehension tasks lack sufficient training data to fully exploit powerful deep neural network models.
Approach: They propose to use QA data to generate more training data for machine reading comprehension tasks by crowdsourcing . they first collect a large-scale multiple-choice QA dataset for Chinese, ExamQA, and then use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance.
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DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications (2021.acl-short)

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Challenge: In order to comprehensively verify the robustness and generalization of MRC models, we construct a real-world Chinese dataset - DuReader_robust .
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Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)

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Challenge: Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data.
Approach: They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs.
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MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (P19-1)

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Challenge: A large number of reading comprehension (RC) datasets have been created, but little research has been done on whether they generalize to one another and the extent to which existing datasets can be leveraged for improving performance on new ones.
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A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020.lrec-1)

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Challenge: Existing literature on machine reading comprehension (MRC) data is limited on the data design of gold standards.
Approach: They propose a framework to investigate linguistic features, lexical cues and ambiguity in MRC gold standards.
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